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RANGE RESOLUTION IMPROVEMENT IN
PASSIVE COHERENT LOCATION RADAR
SYSTEMS USING MULTIPLE FM RADIO
CHANNELS
A THESIS
SUBMITTED TO THE DEPARTMENT OF ELECTRICAL AND
ELECTRONICS ENGINEERING
AND THE INSTITUTE OF ENGINEERING AND SCIENCES
OF BILKENT UNIVERSITY
IN PARTIAL FULLFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTER OF SCIENCE
By
Akif Sinan TAŞDELEN
January 2007
ii
I certify that I have read this thesis and that in my opinion it is fully adequate, in
scope and in quality, as a thesis for the degree of Master of Science.
Prof. Dr. Hayrettin Köymen (Supervisor)
I certify that I have read this thesis and that in my opinion it is fully adequate, in
scope and in quality, as a thesis for the degree of Master of Science.
Prof. Dr. Ayhan Altıntaş
I certify that I have read this thesis and that in my opinion it is fully adequate, in
scope and in quality, as a thesis for the degree of Master of Science.
Prof. Dr. İbrahim Körpeoğlu
Approved for the Institute of Engineering and Sciences:
Prof. Dr. Mehmet B. Baray
Director of Institute of Engineering and Sciences
iii
ABSTRACT
RANGE RESOLUTION IMPROVEMENT IN PASSIVE
COHERENT LOCATION RADAR SYSTEMS USING
MULTIPLE FM RADIO CHANNELS
Akif Sinan Taşdelen M.S. in Electrical and Electronics Engineering
Supervisor: Prof. Dr. Hayrettin Köymen
January 2007
Passive coherent location (PCL) radar systems that use single FM radio channel signal as illuminator of opportunity have reasonable Doppler resolution, but suffer from limited range resolution due to low modulation bandwidth and high dependence on the content that is being broadcasted from the FM station. An improvement in range resolution is obtained by using multiple adjacent FM channels, emitted from co-sited transmitters, which is often the case in large towns. The proposed scheme computes the autocorrelation function of the signal directly received from the co-located FM transmitter, and compares it to the cross-ambiguity function, obtained from direct and target scattered signals. The geometry of the problem is like in the case of monostatic radar. The range information is obtained by the delay between the cross-ambiguity function and the autocorrelation function. When a single FM channel that has a modulation bandwidth of 25 kHz is employed the range resolution is 6 km. It is shown that down to −33dB signal to noise ratio (SNR), which corresponds to a distance of 110 km from the receiver, targets that are 3 km separated from each other can be detected with 3 adjacent FM channels. It is possible to detect targets that are 100 m separated from each other with 7 FM channels. The detection of the time delays is a linear estimation problem.
Keywords: Passive coherent location (PCL), range resolution, modulation
bandwidth, FM radio, cross-ambiguity.
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ÖZET
ÇOĞUL FM RADYO KANALI KULLANILARAK PASİF
TUTARLI KONUMLAMA RADAR SİSTEMLERİNDE
UZAKLIK ÇÖZÜNÜRLÜĞÜ ARTIRMA
Akif Sinan Taşdelen
Elektrik ve Elektronik Mühendisliği Bölümü Yüksek Lisans Tez Yöneticisi: Prof. Dr. Hayrettin Köymen
Ocak 2007
Kooperatif olmayan aydınlatıcı olarak tek FM radyo kanalı kullanan pasif tutarlı konumlama (PTK) radar sistemleri, makul Doppler çözünürlüğüne sahip olmalarına karşı, dar modülasyon bant genişliğinden, ve o an yayınlanın yayının içeriğine olan yüksek bağımlılıklarından dolayı sınırlı uzaklık çözünürlüğüne sahiptirler. FM radyo kanal dağılımının gevşek şekilde denetlendiği bazı ülkelerde bulanabileceği şekilde aynı vericilerden yayınlanan birden fazla FM radyo istasyonu kullanıldığı zaman uzaklık çözünürlüğünde bir gelişme elde edilebilir. Önerilen sistem, aynı yerde bulunan FM vericisinden alınan direk sinyalin öz ilinti fonksiyonunu hesaplar ve olası hedeflerden yansıyarak gelen sinyalin direk sinyalle olan çapraz ilinti fonksiyonuyla karşılaştırır. Problemin geometrisi monostatik radar sistemlerindeki gibidir. Uzaklık bilgisi çapraz ve öz ilinti fonksiyonlarının arasındaki zaman farkından elde edilir. Modülasyon bant genişliği 25 kHz olan tek bir FM istasyonu kullanıldığında 6 km uzaklık çözünürlüğü elde edilmektedir. Bu tezde gösterilmektedir ki, yanyana bulunan 3 FM radyo kanalının sinyallari kullanıldığında sinyal gürültü oranı (SGO) −33dB’ye inene kadar (ki bu 110 km uzaklığa tekamül etmektedir) aralarındaki uzaklık 3 km olan iki hedef başarıyla bulunabilir. 7 FM radyo kanalı ile birbirlerinden 100 m uzakta bulunan iki hedefi tespit etmek mümkündür.
Anahtar Kelimeler:.Pasif tutarlı konumlama, uzaklık çözünürlüğü, modülasyon
bant genişliği, FM radyo, çapraz ilinti
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Acknowledgements
I would like to express my gratitude to Prof. Dr. Hayrettin Köymen for his
supervision, encouragement and guidance throughout the development of this
thesis.
I would also like to thank my family, Servet, Rânâ and Erdem Taşdelen, for
their invaluable support, love and belief in me. I would express my special
thanks to Gözde for her patience, confidence and unconditional love.
It is a pleasure for me to show deep appreciation for my friends in the ‘Synergy
Room’, without whom the development of this thesis would have been much
tiresome. I am also grateful to the guys of Chiba City for their morale support.
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Table of Contents
1. INTRODUCTION .................................................................................. 1
1.1 HISTORY OF PASSIVE COHERENT LOCATION....................................... 2
1.2 ‘ILLUMINATORS OF OPPORTUNITY’ ..................................................... 4
1.3 ORGANIZATION OF THE THESIS........................................................................6
2. RADAR AMBIGUITY FUNCTION .................................................... 7
2.1 THE MONOSTATIC RADAR AMBIGUITY FUNCTION ....................................7
2.2 THE BISTATIC RADAR AMBIGUITY FUNCTION .................................. 10
2.3 FM RADIO BROADCASTING........................................................................... 12
3. PCL USING MULTIPLE FM RADIO CHANNELS ....................... 16
3.1 THE MOTIVATION............................................................................................. 16
3.2 PERFORMANCE PREDICTION .............................................................. 18
3.3 MINIMUM SNR CONDITION FOR DIRECT SIGNAL RECEPTION ........... 28
3.4 DETECTION........................................................................................ 30
4. RANGE RESOLUTION IMPROVEMENT...................................... 35
4.1 RANGE RESOLUTION LIMITATIONS WHEN SINGLE FM CHANNEL IS
EMPLOYED.............................................................................................. 35
4.2 RANGE RESOLUTION IMPROVEMENT WHEN 3 FM CHANNELS ARE
EMPLOYED.............................................................................................. 41
4.3 RANGE RESOLUTION IMPROVEMENT WHEN 7 FM CHANNELS ARE
EMPLOYED.............................................................................................. 45
5. CONCLUSIONS................................................................................................... 47
6. APPENDIX A......................................................................................................... 49
7. APPENDIX B......................................................................................................... 51
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List of Figures
Figure 2.1 North referenced coordinate system ................................................. 11
Figure 2.2 Pre-emphasis filter fequency response.............................................. 13
Figure 2.3 Stereo FM radio spectrum................................................................. 13
Figure 2.4 One sided energy spectral density of kanun and ney duet ................ 14
Figure 2.5 One sided energy spectral density of FM modulated signal, message
signal being kanun and ney duet ................................................................ 14
Figure 2.6 One sided energy spectral density of Carmina Burana – O Fortuna. 15
Figure 2.7 One sided energy spectral density of FM modulated signal, message
signal being Carmina Burana – O Fortuna ................................................. 15
Figure 3.1 Part of FM frequency spectrum recorded at Bilkent University,
Ankara. Signals are transmitted from Çaldağı, Ankara.............................. 17
Figure 3.2 Square wave ...................................................................................... 19
Figure 3.3 Spectrum of the square wave ............................................................ 20
Figure 3.4 Spectrum of the sum of a base-band square wave and a modulated
square wave ................................................................................................ 20
Figure 3.5 Autocorrelation of the sum of a base-band square wave and a
modulated square........................................................................................ 21
Figure 3.6 One-sided spectrum of the message signal ....................................... 22
Figure 3.7 One-sided spectrum of the FM modulated signal ............................. 22
Figure 3.8 Ambiguity function of a single FM channel waveform.................... 23
Figure 3.9 One-sided spectra of the three message waveforms ......................... 24
Figure 3.10 One-sided spectrum of the signal containing 3 FM channels ......... 24
Figure 3.11 Ambiguity function of three adjacent FM channel waveform........ 25
Figure 3.12 Autocorrelation functions of single FM channel waveform and three
adjacent FM channels waveform................................................................ 26
Figure 3.13 Autocorrelation function for seven adjacent FM stations............... 27
Figure 3.14 Autocorrelation function for seven non-adjacent FM stations ....... 28
viii
Figure 3.15 Autocorrelation function for single FM signal for various SNR .... 39
Figure 3.16 Autocorrelation function for seven adjacent FM signals for various
SNR ............................................................................................................ 30
Figure 3.17 Block diagram of the detection algorithm ...................................... 33
Figure 4.1 The autocorrelation of the directly received signal........................... 36
Figure 4.2 The cross-correlation of the direct signal and target scattered signal37
Figure 4.3(a) One-sided spectrum of the autocorrelation of the direct signal. (b)
One-sided spectrum of the cross-correlation of the direct and target
scattered signals. (c) One-sided spectrum of the signal resulting from the
division of auto- and cross-correlation spectrums after filtering operation 38
Figure 4.4 Real part of the IFFT of the division signal (1FM channel employed,
targets are 6 km separated, rectangular filter) ............................................ 38
Figure 4.5 Tapered division signal ..................................................................... 39
Figure 4.6 Real part of the IFFT of the division signal (1FM channel employed,
targets are 6 km separated, tapered division signal)................................... 39
Figure 4.7 Real part of the IFFT of the division signal (1FM channel employed,
targets are 3 km separated, rectangular filter) ............................................ 40
Figure 4.8 Real part of the IFFT of the division signal (1FM channel employed,
targets are 3 km separated, tapered division signal)................................... 41
Figure 4.9 IFFT of the Division Signal after rectangular filtering..................... 42
Figure 4.10 Real part of the IFFT of the division signal (3FM channels
employed, targets are 6 km separated, rectangular filter) .......................... 42
Figure 4.11 Real part of the IFFT of the division signal (3FM channels
employed, targets are 6 km separated, tapered division signal .................. 43
Figure 4.12 Real part of the IFFT of the division signal (3FM channels
employed, targets are 3 km separated, rectangular filter) .......................... 44
Figure 4.13 Real part of the IFFT of the division signal (3FM channels
employed, targets are 3 km separated, Gaussian filter).............................. 45
Figure 4.14 Real part of the IFFT of the division signal (7FM channels
employed, targets are 100 m separated) ..................................................... 45
1
Chapter 1
Introduction
On February 26, 1935 Robert Watson-Watt, an English scientist, conducted a
simple experiment in a field near Daventry, UK, that proved that aircrafts can
successfully detected by radio waves. The idea was that if an aircraft was
'illuminated' by radio waves, enough energy should be 'reflected' to permit
detection on the ground by a sensitive receiver. The BBC had a powerful short-
wave transmitter Station near Daventry, the Empire Radio Station, with a power
output of 10 kW. The wavelength was 49m and the continuous beam was about
30 degrees wide and with a 10 degree elevation. An old Hadley Page Heyford
bomber made a number of passes at differing altitudes from 6000 ft down to
1000 ft. The demonstration was a success: on several occasions a clear signal
was seen from the bomber. This experiment, which is historically known as the
Daventry Experiment, is, besides being the first radar experiment, a passive
radar experiment [1, 2, 3, 4].
Passive radar systems, also known as passive coherent location (PCL) and
passive covert radar, are radar systems that use non-cooperative illuminators,
also referred as ‘illuminators of opportunity’, as sources of target illumination.
Instead of transmitting optimally shaped waveforms with a dedicated
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transmitter, which is the case in conventional radar systems, passive radar makes
use of the telecommunication signals that are already in the air, such as analogue
television or radio broadcast signals, GSM telephone signals etc (not to be
confused with passive systems that perform imaging based on thermal body
radiation).
The largest benefit of using non-cooperative illuminators is that it is almost
impossible to detect, since the system has no transmitter of its own (this is where
the name ‘passive’ comes from). It is a low cost system. Since the transmitter
network is already built, the deployment and maintenance is easy. There is no
frequency allocation needed.
The largest drawback of the system is that the waveform being transmitted and
its power are not controllable. Therefore, the system has to operate with signals
that may not be optimal for radar purposes. This means that high computational
power is needed.
1.1 History of Passive Coherent Location
After the Daventry Experiment radar systems became a major field of research.
Early radar systems were all bistatic, where the transmitter and receiver are not
co-located. This was due to the fact that the technology to switch an antenna
from transmitter to receiver mode was not developed yet. Examples like the
British Chain Home system; the French “fence” system; the Soviet RUS-1; and
the Japanese “Type-A” were all systems that had their own transmitters [5].
The Germans used a passive bistatic system, which they called the Kleine
Heidelberg device, during World War II. The system was located at Ostend and
was using the British Chain Home radar system as the non-cooperative
illuminator to detect aircraft over the southern part of the North Sea [5].
3
Later, systems that designed and transmitted their own optimally shaped
waveforms that could have high transmitted signal powers became dominant.
These are now called conventional radars. Because of the high computational
power requirement, passive coherent location radars became unattractive for
researchers.
With the introduction of low cost computing power and digital receiver
technology in the 1980’s the concept of passive radar systems became a popular
research area, again. Now the designers could apply digital signal processing
techniques to exploit variety of broadcast signals. The first commercial system
that was announced by Lockheed-Martin Mission Systems in 1998 was the
Silent Sentry that exploited FM radio and analogue television transmitters [6].
Besides Silent Sentry, currently there are working passive radar systems like
BAe System’s CELLDAR that uses GSM basestations as illuminator of
opportunity [7] and Thales Air Systems’ Homeland Alerter, an FM radio based
passive radar.
The researches focus on two major topics: The problem of the geometry and the
problem of the signal processing.
In [8] a methodology for finding possible receiver locations for passive radar is
presented. It is stated that, because of the wide range of passive radar
applications an optimal receiver location cannot be determined, but there are
optimal solutions for each specific requirement. In [9] the constellation of
multisensors in bearing-only location system is studied. In [10] a target location
method based on time difference of arrival (TDOA) sequences with a two
transmitter/one receiver (T2/R) passive radar is presented.
There are numerous techniques suggested for passive radar imaging. A
Smoothed Pseudo Wigner-Ville Distribution (SPWVD)-based imaging
4
algorithm can be applied to reflected TV signals for target classification [11].
Some deconvolution techniques, such as the CLEAN algorithm, which is
utilized for high-resolution target imaging at high-frequencies, can be applied to
frequencies of interest in passive radar. This yields images that are more
disturbed [12]. In [13] an optimization-based, region-enhanced image formation
technique for the sparse aperture passive radar is explored. Compared to
conventional direct Fourier transform-based imaging passive radar data, where
artifacts are produced and characteristic features of the imaged objects are
suppressed, the region-enhanced imaging approach is well suited for passive
radar imaging. Roland Mahler’s probability hypothesis density (PHD) based
multitarget tracking is investigated in [14]. To partially overcome the problem of
high computational power requirement distributed passive radar processing
through channelised data is suggested in [15].
1.2 ‘Illuminators of Opportunity’
Broadcast signals such as analogue television, FM radio and Digital Audio
Broadcast (DAB) have high transmitter powers and excellent coverage. Due to
these properties, they are attractive illuminators of opportunity for PCL
applications.
At first glance, analogue television transmitters seem to be the obvious choice,
because of their very high equivalent radiated powers and large bandwidth.
However, the 64 µs line fly back time creates high side-lobes in the ambiguity
function, resulting in range ambiguities at 19.2 km [16]. Using only a fraction of
the analogue TV spectrum, which is very difficult due to the 15 kHz separation
of the line fly backs, is not beneficial, since the power of each line fly back is
very low compared to the total power. Howland showed that it is possible to
extract Doppler and bearing information from echoes of analogue TV video
carrier signal to track aircrafts at ranges up to 260 km from the receiver and 150
km from the transmitter [17]. Howland took the receiver bandwidth only a few
5
kilohertz, which is only a fraction of the total 5.5 MHz analogue TV spectrum.
Therefore, this approach is named as ‘narrowband processing’. Due to the low
information content in the Doppler measurements, the system must observe the
target’s Doppler history for an extended period of time in order to locate the
target at all. Furthermore, the location depends on the initial location estimate of
the target.
In [18] the suitability of Global System of Mobile (GSM) communication
signals for passive radar applications is investigated. It is shown by an
implemented system that GSM based PCL can successfully detect and track
ground-moving targets such as vehicles and humans. However, due to the low
radiated power GSM signals are not well suited for air-borne target detection.
Digital broadcast signals such as Digital Television (DTV), Digital Audio
Broadcast (DAB) and Digital Video Broadcast (DVB) are also sources of non-
cooperative illumination. In [19] DAB is shown to have a coverage of around 9
km (where the SNR drops to 15dB). Poullin states in [20] that he has achieved a
range resolution of 200 m with DAB. In [21] Saini and Cherniakov show that
deterministic components in the DTV-T transmission introduce a number of
undesired peaks in the ambiguity function that they try to eliminate with some
algorithms. They conclude that DTV-T is a promising candidate for passive
radar applications.
In [22] the global navigation satellite system (GNSS) is used as the non-
cooperative illuminator. Two basic configurations are considered: the radar
receiver is on an airplane; and positioned stationary on the ground. The system
tries to detect objects on the ground. Another research on PCL using non-
cooperative satellite (e.g. Globalstar or Global Positioning System (GPS)) as
illuminator is [23] that focuses on the 2-D resolving capability within the ground
plane.
6
In contrast to the narrowband processing, wideband processing utilizes almost
all the spectrum of the waveform being exploited at the receiver. A typical
single station FM broadcast waveform has 100 kHz bandwidth, which
corresponds to a theoretical range resolution of 1500 m. Griffiths showed,
however, that the effective bandwidth is not the whole 100 kHz, depending on
the content being broadcasted, it can reach up to 24.4 kHz on the average for
fast tempo jazz music, that shows the highest bandwidth [16]. Ambiguity plots
that show on the average 30dB – 40dB peak to side-lobe ratios in range, and
30dB – 50dB peak to side-lobe ratios in Doppler demonstrate the performance
of FM radio waveforms in PCL. These show that FM based PCL has a
reasonable Doppler resolution, but a poor range resolution. In [24], Howland
showed that an experimental radar system using a single station FM radio
broadcast signal as illuminator of opportunity detects and tracks targets to
ranges in excess of 150 km from the receiver. In [25] it is shown that FM based
PLC can also be employed to observe auroral E-region irregularities.
1.3 Organization of the Thesis This thesis is organized as follows: Chapter 2 explains the basics of radar
ambiguity function for both monostatic and bistatic radar and the basics of FM
radio broadcast. Chapter 3 introduces the idea of multiple FM radio channel
PCL and investigates the performance in detail. In Chapter 4 simulations with 1
FM, 3 FM and 7 FM are done for closely separated targets. Finally, Chapter 5
concludes the thesis.
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Chapter 2
The Radar Ambiguity Function
According to the geometry of the transmitter-receiver positioning, radar systems
can be divided into three groups: Monostatic radar, bistatic radar and multistatic
radar. Radar systems, where the transmitter and the receiver are co-located are
called monostatic systems. Monostatic systems have the advantage of simple
geometry. If the transmitter and receiver are located in different places, the
system is called bistatic. Bistatic receivers systems have higher detection ability
than monostatic radars when the head-on radar cross section of the target is
small, and when the target scatters incident energy in different directions than
the incident direction, which is the case in stealth fighters [26]. Multistatic radar
systems are bistatic systems that use multiple receivers.
2.1 The Monostatic Radar Ambiguity Function The radar ambiguity function is a well established measure in evaluating how
well a waveform is suited for radar purposes [27]. It represents the output of a
matched filter, where the signal is received with a time delay, τ, and a Doppler
shift, υ, relative to the zeros expected by the filter [28]. In other words, the
ambiguity function shows how similar a waveform is to its delayed versions in
time and to its Doppler shifted versions in frequency. The radar ambiguity
function is defined as:
( )2
2 )2exp()()(, ∫∞
∞−
∗ ⋅+⋅=Α dttjtsts πυτυτ (2.1)
The ambiguity function evaluated at (τ, υ) = (0, 0) is the output of a filter, which
is perfectly matched to the received signal. It is shown in APPENDIX A that the
value of the ambiguity function at this point is its global maximum.
8
22 )0,0(),( AA ≤υτ (2.2)
Thus, the three dimensional plot of the ambiguity function shows a peak at the
origin.
Another useful property of the ambiguity function is that it is symmetric with
respect to the origin. 22 ),(),( υτυτ −−= AA (2.3)
This property shows that it is sufficient to study and plot only two adjacent
quadrants of the ambiguity function.
The zero cuts of the ambiguity function, )0,(τA and ),0( υA , give insight into
the range and Doppler properties of the waveform respectively.
∫∞
∞−
∗ +⋅= dttstsA )()()0,( ττ (2.4)
can be recognized as the autocorrelation function of the waveform. The
autocorrelation function is a measure for the range resolution.
The minimum separation between two targets required for radar to distinguish
between them is called the range resolution. Range resolution is a function of
modulation bandwidth. The larger the modulation bandwidth of the waveform
used in the radar the smaller is the range resolution. Since two closely spaced
targets would reflect the incident waveform with a small delay in between, to
distinguish those targets as two separate ones, the autocorrelation of the
waveform has to have a sharp peak. In other words, the waveform and its
delayed version should be ‘separated enough’ to distinguish the two reflections
as two separate peaks in the ambiguity function. Equivalent to the range
resolution, the delay resolution can be defined as the minimum time difference
of two targets that can be successively resolved. Since waves travel at the speed
of light, c, delay resolution can be obtained by dividing range resolution to c.
9
The motion of the target creates a Doppler shift in the frequency of the signal.
The minimum Doppler shift required to distinguish between two targets is called
the Doppler resolution. Doppler resolution is a function of the waveform and
target illumination time. Alternatively, a velocity resolution can be defined as
the minimum velocity difference between two targets to resolve them
successfully. Velocity resolution can be obtained from the Doppler resolution
by:
λυ∆
=∆2v (2.5)
∆v: velocity resolution
∆υ: Doppler resolution
λ: wavelength
Like in the range ambiguity, a sharp peak in ),0( υA is desired to better resolve
two targets that have Doppler shifts close to each other (that are moving with a
small speed difference).
Considering requirements both for range and Doppler, the best possible
waveform would have an ambiguity function, which is a delta function at the
origin, δ(0,0), with amplitude (2E)2, where E is the signal energy. The
multiplicative constant is due to the constant volume property (see APPENDIX
A). A signal with this property could locate the target at exact range and
velocity. However, a signal having such an ambiguity function would require
infinite bandwidth and infinite duration, which is practically impossible. Thus,
regarding the application of the designed radar, ambiguity functions that
approach δ(0,0) shape should be aimed.
Another way to define the monostatic ambiguity function is that it represents the
Neyman-Pearson receiver. This receiver maximizes the detection probability for
a specified false alarm probability, for detecting a slowly fluctuating point target
at the hypothesized point (τH, ωH). The actual target location is (τa, ωa). In this
10
receiver, the ambiguity function is to be tested under two hypotheses, and
compared to a threshold. The unknown nonrandom parameters τH, and ωH are to
be estimated. The common expression for the ambiguity function is
( ) ( )2
)exp()(~)(~,,, ∫∞
∞−
∗ −−⋅−⋅−= dttjtstsA aHHaaHaH ωωττωωττ (2.6)
Comparing equation (2.6) with equation (2.1) we note that Ha τττ −=
and Ha ωωπυ −=2 . Like mentioned above, τ is the time delay difference
between the actual and hypothesized time delays. The autocorrelation reaches its
maximum when τ = 0, meaning that the actual target delay is estimated.
Likewise, υ is the difference between the actual Doppler shift and the
hypothesized Doppler shift. The ambiguity function reaches its maximum when
(τ, υ) = (0, 0).
2.2 The Bistatic Radar Ambiguity Function In PCL applications receiver may, and in most cases will not be co-located with
the illuminator. Therefore, the relative positions of transmitter, receiver and
target plays a crucial role in ambiguity function calculation.
In [29] a geometry dependent definition of the Bistatic Ambiguity Function is
introduced. This can be better be described by reffering to the north referenced
coordinate system is given in Figure 2.1.
11
Figure 2.1. North-referenced coordinate system
The bistatic angle β is the angle at the apex of the bistatic triangle, and it is the
sum of the transmitter’s look angle θT and the receiver’s look angle -θR. When
the target is in the north of the baseline, β is in the interval [0,π]; when the target
is to the south of the baseline, then β is in the interval [-π,0]. The line dividing
the bistatic angle into two equal parts is called the bistatic bisector. RR and RT
are the ranges from receiver to target and from transmitter to target, respectively.
The sum of both ranges is called the total range, and is represented by R.
Any three of the four quantities θT, θR, L and R can completely specify a bistatic
radar geometry. For instance
RRRT LRLRR θsin2222 ++= . (2.7)
Therefore the total transmission time delay is given by
( ) [ ] cLRLRRLR RRRRRR θθτ sin2,, 22 +++= . (2.8)
Likewise, the effective velocity is the sum of individual velocities.
⎟⎠⎞
⎜⎝⎛=+=
2coscos2 βφVR
dtdR
dtdR
dtd
RT (2.9)
From trigonometry and law of cosines
12
RRR
RR
LRLR
LR
θ
θββ
sin22
sin21
2cos1
2cos
22 ++
++=
+=⎟
⎠⎞
⎜⎝⎛ (2.10)
is obtained. Thus the Doppler shift for a given target with velocity component
along the bistatic bisector φcos2V , is given by
( )RRR
RRcRRD
LRLR
LRV
cf
LVRfθ
θφθφ
sin22
sin21cos2,,cos,
22 ++
++= (2.11)
Given equations (2.8) and (2.11) the bistatic ambiguity function can be
expressed by
( ) ( )( ) ( )( )
( ) ( )( )( ) 2,,,,,,exp
,,~,,~,,,,,
dttLVRLVRj
LRtsLRtsLVVRRA
RaRaRHRH
RRHRRaRaHRR
aH
HaaH
θωθω
θτθτθ
−−
−⋅−= ∫∞
∞−
∗
(2.12)
2.3 FM Radio Broadcasting Invented in early 1930s, FM radio broadcasting became popular starting 1940s
and is the most widely used modulation technique in radio broadcast. Although
digital audio broadcast (DAB) starts to replace FM radio in several countries, it
seems that it will still be used in developing countries for the near future.
APPENDIX B contains the basics of frequency modulation.
FM radio broadcast utilizes the 87.5-108 MHz frequency band for voice and
music transmission. The carrier frequencies of two adjacent channels are
separated 100 kHz in some countries like UK, and 200 kHz in other countries
like USA. However, radio stations that are broadcasted from co-sited
transmitters are at least 300 kHz separated. In Turkey the separation is 200 kHz,
but weakly controlled. The peak frequency deviation is fixed as 75± kHz. In
most countries the modulated signal is passed through a pre-emphasis filter,
whose frequency response is given in Figure 2.2, to increase the performance of
the demodulator in the presence of noise. The intermediate frequency, IFf used
in the up and down conversion is 10.7 MHz.
13
Figure 2.2. Pre-emphasis filter frequency response
The radio stations that transmit stereo radio music process the message signal
before modulation. The sum and the difference of left and right microphone
outputs are taken and a pilot tone is added for the separation process as seen in
Figure 2.3. In stereo FM broadcast the bandwidth of the message signal is larger
than in monophonic broadcast.
Figure 2.3. Stereo FM radio spectrum [30]
Below are two examples of monophonic wave files that are 0.1 seconds in
length. First file is a kanun and ney (two traditional Turkish instruments) duet
witch has a low bandwidth as seen in Figure 2.4. When this file is FM
modulated, with carrier frequency 100 kHz, frequency deviation 50 kHz, the
outcome is seen in Figure 2.5.
14
0 5000 10000 150000
10
20
30
40
50
60
70
frequency (Hz)
amplitu
de
Energy Spetral Density of file1
Figure 2.4. One sided energy spectral density of kanun and ney duet
0.4 0.6 0.8 1 1.2 1.4 1.6
x 105
0
200
400
600
800
1000
1200
1400
1600
1800
2000
frequency (Hz)
amplitu
de
Energy Pectral Density of FM modulated file1
Figure 2.5. One sided energy spectral density of FM modulated signal, message signal being
kanun and ney duet
In Figure 2.6 the energy spectral density of another file with a full orchestra,
playing Karl Orff’s Carmina Burana – O Fortuna, is depicted. The bandwidth is
much wider. As seen from Figure 2.7, this waveform FM modulated has a wider
modulation bandwidth than the first one.
15
0 5000 10000 150000
10
20
30
40
50
60
70
frequency (Hz)
amplitu
de
Energy Spectral Density of file2
Figure 2.6. One sided energy spectral density of Carmina Burana – O Fortuna
0.4 0.6 0.8 1 1.2 1.4 1.6
x 105
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Energy Spectral Density of FM modulated file2
frequency (Hz)
amplitu
de
Figure 2.7. One sided energy spectral density of FM modulated signal, message signal being
Carmina Burana – O Fortuna
16
Chapter 3
PCL Using Multiple FM Radio Channels
3.1 The Motivation The range resolution in constant false alarm rate (CFAR) radar systems is
inversely proportional to the bandwidth of the waveform that is being used.
BcresolutionRange
2= (3.1)
In the above equation, c is the speed of light and B is the bandwidth of the
signal. This relation suggests that range resolution in PCL systems that use FM
radio broadcast as ‘illuminator of opportunity’ can be improved by increasing
the total modulation bandwidth of the waveform. This can be done by making
use of the signals of as many channels as required to achieve the aimed range
resolution. The commercial FM radio broadcast spectrum 88-108 MHz suggests
that hypothetically 20 MHz band can be exploited. Even if the modulation
bandwidth corresponds to 10% of the total spectrum, 2 MHz, range resolution
down to 75 m can be achieved.
In one service area, the carriers are separated at least by 300 kHz. In countries,
where the FM radio channel allocations are weakly regulated, the maximum
frequency deviations of the channels are faintly controlled. Frequencies that fall
into the adjacent channel are not adequately suppressed, causing in metropolitan
towns spectrum readings as seen in Figure 3.1. Interference between adjacent
channels, are beneficial for the proposed system, although not desired in
telecommunication. For instance, in Figure 3.1, the entire 94.7-95.6 MHz
17
spectrum or 96.5-97 MHz spectrum can be made use of, which has larger
bandwidth than a single FM channel, yielding a better range resolution.
Figure 3.1. Part of FM frequency spectrum recorded at Bilkent University, Ankara. Signals are
transmitted from Çaldağı, Ankara
In the above case, if the frequency band of 94.7-95.6 MHz is used we have a
bandwidth of 900 kHz. As seen from equation 3.1, a bandwidth of 900 kHz
results in a range resolution of 167 m. However, this is the case if the spectrum
has equal power distribution. The power spectral density of the signal used is a
slightly deviated version of triangular spectra. Thus the range resolution is
expected to be somewhat larger than 167 m.
18
3.2 Performance Prediction Due to Fourier transform property of the ambiguity function (see APPENDIX
A)
( ) ( ) ∫∞
∞−
∗ −+=−= dffjfSfSAA XX )2exp()()(,, τπυτυυτ (3.2)
where S(f) is the Fourier transform of the signal s(t). Assuming the FM radio
channels transmit the same content with same power, the spectrum of the signal
being used is a sum of frequency shifted triangles, where each shift is equal to
the carrier frequency of the individual channel. Thus, if n radio channels are
used in the process the spectrum is represented by
∑=
−=n
icn iffSfS
0)()( (3.3)
where S(f) is the spectrum of an individual FM radio channel, and fc is the
carrier spacing. Therefore the resulting ambiguity function is
( ) ∫∞
∞−
∗ −+=− dffjfSfSA nnXX )2exp()()(, τπυτυ (3.4)
( ) ∫∞
∞−
∗ += dttjtstsA nn )2exp()()(, πυτυτ (3.5)
where
)2exp()(
)2exp()(
)2exp()()(
0
0
tifjts
dfftjiffS
dfftjfSts
c
n
i
n
ic
nn
π
π
π
∑
∫ ∑
∫
=
∞
∞− =
∞
∞−
=
⎟⎠
⎞⎜⎝
⎛−=
=
(3.6)
Thus,
( )
dttjtifjts
tifjtsA
c
n
i
c
n
i
)2exp()2exp()(
)2exp()(,
0
0
πυπτ
πυτ
⎟⎠
⎞⎜⎝
⎛−+
⎟⎠
⎞⎜⎝
⎛=
∑
∫ ∑
=
∗
∞
∞− = (3.7)
19
The shape of the ambiguity function for n FM radio channels is bounded by the
ambiguity function of one FM channel. This result tells us that we can only
improve by adding new radio channels.
Since the range resolution can be obtained from the autocorrelation function of
the transmitted signal, it is beneficial to further investigate the properties of the
autocorrelation function.
Assume we have a time limited signal s(t), as seen in Figure 3.2
⎪⎩
⎪⎨
⎧
<<<−
−<=
tTTtT
Ttts
p
pp
p
2,022,1
2,0)(
Figure 3.2. Square wave
The spectrum of s(t), S(f) is depicted in Figure 3.3.
20
Figure 3.3. Spectrum of the square wave
We note that the first zero occurs at 1/Tp. If a signal of the same spectral
characteristics, but centered at fc = 6/Tp, is added to this signal, then we obtain
the signal sT(t), with the spectrum given in Figure 3.4. This is equivalent to
adding s(t)cos(2πfct) to s(t).
Figure 3.4. Spectrum of the sum of a base-band square wave and a modulated square wave
21
The resulting signal sT(t), has an autocorrelation function as depicted in Figure
3.5.
Figure 3.5. Autocorrelation of the sum of a base-band square wave and a modulated square wave
As seen from Figure 3.5, the first notch occurs at Tp/12 = 1/(2fc). Thus, we
observe again, by increasing the total bandwidth the peak can be made narrower.
Also we note that the envelope of the autocorrelation function of sT(t) is the
autocorrelation function of s(t), i.e. a triangle.
In the following simulation the message signal is a 15 kHz filtered monophonic
music file of 100 ms in length. The one-sided spectrum of the message signal is
given in Figure 3.6.
22
0 5000 10000 150000
50
100
150
200
250
300
350Spectrum of the message signal
frequency (Hz)
amplitu
de
Figure 3.6. One-sided spectrum of the message signal
The FM signal has a maximum frequency deviation of ± 75 kHz with no pre-
emphasis. The one-sided spectrum of the frequency modulated signal is depicted
in Figure 3.7.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 105
0
2000
4000
6000
8000
10000
12000Spectrum of the FM modulated signal
frequency (Hz)
amplitu
de
Figure 3.7 One-sided spectrum of the FM modulated signal
23
The ambiguity function of a frequency modulated signal is given in Figure 3.8.
Since the ambiguity function is symmetric for this signal, only a quarter is
plotted.
Figure 3.8. Ambiguity function of a single FM channel waveform
In the following simulation three different waveforms of 100 ms length are
employed as message signals. The one-sided spectra of the waveforms are
depicted in Figure 3.9.
24
0 5000 10000 150000
100
200
300
400Spectrum of the sound file1
frequency (Hz)
ampl
itude
0 5000 10000 150000
100
200
300Spectrum of the sound file2
frequency (Hz)
ampl
itude
0 5000 10000 150000
200
400
600Spectrum of the sound file3
frequency (Hz)
ampl
itude
Figure 3.9. One-sided spectra of the three message waveforms
The one-sided spectrum of the signal containing 3 FM channels having 100 kHz
equally spaced carrier frequencies is given in Figure 3.10.
0 0.5 1 1.5 2 2.5 3 3.5 4
x 105
0
2000
4000
6000
8000
10000
12000Spectrum of the 3 equally spaced FM waveforms
frequency (Hz)
ampl
itude
Figure 3.10. One-sided spectrum of the signal containing 3 FM channels
25
The ambiguity function of the signal containing three adjacent FM channels is
depicted in Figure 3.11.
Figure 3.11. Ambiguity function of three adjacent FM channel waveform
It is observed that the ambiguity function of the signal containing a single FM
channel is an envelope to the ambiguity function of three FM station signal.
The range resolution obtained by using only one FM channel, and the range
resolution obtained from three adjacent stations that broadcast different content
are depicted in Figure 3.12. Both signals have message contents with similar
bandwidth.
26
-150 -100 -50 0 50 100 150-30
-25
-20
-15
-10
-5
0
range (km)
ampl
itude
(dB
)
-20 0 20-10
-8
-6
-4
-2
01 FM3 FM
Figure 3.12. Autocorrelation functions of single FM channel waveform and three adjacent FM
channels waveform
The envelope of the autocorrelation function for three adjacent FM stations is
the autocorrelation function for a single FM station. This is due to the fact that
the spectrum of three adjacent FM station signals is almost like a 100 kHz
repetition of a single FM station spectrum. Due to the periodic structure in the
spectrum, a sinc type formation is observed in the range resolution plot. Since
the modulation bandwidth is about three times the bandwidth of a single FM
transmission, the peak in the autocorrelation function is narrower.
The peak can be made narrower when seven adjacent stations are used together
(Figure 3.13.)
27
-150 -100 -50 0 50 100 150-30
-25
-20
-15
-10
-5
0
range (km)
ampl
itude
(dB)
-10 0 10
-10
-5
0
Figure 3.13. Autocorrelation function for seven adjacent FM stations
As in the three FM station case, the shape of the autocorrelation function of the
waveform from seven adjacent FM stations resembles the autocorrelation plot of
a single FM transmission. However some side-lobes are increased. Again, a sinc
type formation is observed in the range resolution plot.
If stations are selected in a random way so that they are not adjacent, then the
autocorrelation starts to lose its resemblance to the autocorrelation function of a
single FM waveform. The autocorrelation function of seven FM signals having
equal power, but distributed randomly in 1500 kHz bandwidth with minimum
separation of 150 kHz and maximum separation 250 kHz is depicted in Figure
3.14.
28
-150 -100 -50 0 50 100 150-30
-25
-20
-15
-10
-5
0
range (km)
ampl
itude
(dB
)
-10 0 10-10
-5
0
Figure 3.14. Autocorrelation function for seven non-adjacent FM stations
As expected, the sinc type formation in the range resolution is lost due to the
failed periodicity and the side-lobes in the vicinity of the peak are increased.
However, the main peak is still narrow. Theoretically, the range resolution can
be improved down to 2 km with an appropriate detection algorithm.
3.3 Minimum SNR Criterion for Direct Signal Reception In the analysis of the auto-ambiguity and autocorrelation functions the scattered
signal is assumed to be noise free. As the received power of the scattered signal
becomes smaller, the SNR decreases for far away targets. Consequently, the
ambiguity function deteriorates, yielding a worse range resolution or even target
loss or false alarm.
29
The proposed detection method depends on the minimum SNR that allows a
successful detection of the autocorrelation function, buried in the cross-
correlation. This is discussed in 3.4.
In the following simulations, the receiver noise is taken as white and Gaussian
distributed, with spectral height of kT0B, where k is the Boltzmann constant, T0
is the temperature in Kelvin (taken as 300°K ) and B is the bandwidth. The
bandwidth is 200 kHz in single FM signal simulation and 800 kHz in seven FM
signals simulation, where the carriers are spaced at multiples of 100 kHz, and
signal powers are equal. The signals are sampled at 5 MHz. Simulations show
that for a single FM signal, the autocorrelation function preserves its shape
down to −19dB SNR. For lower SNR the cross-correlation terms between signal
and noise start to dominate. However, the peak of the autocorrelation is
dominant down to −29dB as shown in Figure 3.15.
-60 -40 -20 0 20 40 60-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
range (km)
ampl
itude
(dB
)
-9.2 dB-19.2 dB-29.2 dB-39.2 dB
Figure 3.15. Autocorrelation function for single FM signal for various SNR
30
If the number of FM signals is increased, the autocorrelation shape shows no
variation down to −6dB. The changes until −26dB are small and on the side-
lobes. At −36dB the peak is no more dominant over the side-lobes (Figure 3.16).
-60 -40 -20 0 20 40 60-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
range (km)
ampl
itude
(dB
)
-6.8 dB-16.8 dB-36.8 dB
Figure 3.16. Autocorrelation function for seven adjacent FM signals for various SNR
This suggests that the direct signal receiver, if bistatic configuration is
employed, should be at a distance to the FM transmitter, where SNR is at least
−16 dB.
3.4 Detection In [24] it is shown that the Doppler resolution of a PCL system, using FM radio
broadcast as non-cooperative illuminator, is typically 1 Hz, corresponding to a
velocity resolution of around 1.5 ms-1. The Doppler resolution can be improved
by taking a longer integration time. In the analysis below it is assumed that the
Doppler shift of the target is found by the radar and the signal is perfectly
corrected in frequency.
31
Assume s(t) is broadcasted from a FM radio transmitter and received from a co-
located receiver. This direct signal, s(t) at the receiver has a high SNR. The
signal reflected from N targets and received from the receiver is represented by
)()()(1
tnttstsN
iiiN +−= ∑
=
α (3.8)
where αi is the attenuation constant for the signal scattered from the ith target, ti
is the two-way time delay of the signal scattered from the ith target and n(t) is the
zero mean white Gaussian noise process. The result of the cross-correlation of
direct and target scattered signals is given by
)()()()(1
τττατ sntRCN
iii •+−= ∑
=
(3.9)
where• denotes convolution and R is the autocorrelation of the direct signal and
ns is the cross-correlation of the direct signal and noise. Taking the Fourier
transform of the cross-correlation function, we obtain
{ } )()()2exp()( 01
fSNRFftjfCN
iii +−= ∑
=
τπα (3.10)
where F{R(τ)} is the Fourier transform of the autocorrelation function (the
power spectral density), N0 = kT0B is the power spectral height of the white
noise and S(f) is the Fourier transform of the direct signal. Dividing both sides
by |S(f)|2 and applying the autocorrelation property of the Fourier transform we
obtain
20
12 )(
)()2exp(
)()(
fS
fSNftj
fSfC N
iii +−= ∑
=
πα (3.11)
)()2exp( 0
1 fSN
ftjN
iii ∗
=
+−= ∑ πα (3.12)
where * denotes the complex conjugation. The problem reduces to finding the
time shifts ti in the exponential. This is a linear estimation problem, and very
good estimators are available. In the estimation problem for single channel FM
signal, the estimation is done around one frequency, whose width determines the
range resolution. The SNR around this frequency is higher than in other
32
frequencies. If, for instance, 3 FM channels are used, the estimation can be done
around three frequencies, which implies that the range resolution is improved.
The total signal power increases, but due to the additional bandwidth the noise
power increases too. The total SNR, therefore, does not change. This results in
the same detection performance as in the single FM channel case.
Taking the inverse Fourier transform of Equation 3.12 around the above
mentioned frequencies is another way to find the time shifts ti. Due to the
exponential term, the real part of the resulting function shows peaks at correct ti
locations. In this approach however, time shifts can only be successfully
extracted, where the additive term in Equation 3.12 is very small compared to
the exponentials. The term in question is the inverse of the squareroot of SNR,
and it is small at frequencies where the signal power is highest, namely at the
vicinity of the carrier frequency. Thus, the frequencies where the SNR is low
should be avoided in the computation. Effective filtering of the unwanted
frequencies is another issue. While the easiest approach is to utilize bandpass
filters with rectangular spectra, this can cause a sinc type shape in the inverse
Fourier operation. Gaussian tapered or raised cosine shaped filters can be
employed for better results. The detection algorithm is summerized in Figure
3.17.
33
Figure 3.17. Block diagram of the detection algorithm.
In the simulations of this thesis, the SNR is calculated as the total received
signal power divided by the noise power after the filtering at the reception. The
SNR at the detection stage is higher than the SNR at the reception, because the
filter in the detection stage is carrier centered and has a bandwidth equal to the
modulation bandwidth. This means that the total signal power remains almost
the same, whereas the noise power is reduced. For example, when a single FM
channel signal that is transmitted with 1kW power is reflected from two targets
with RCS 10 m2 at 50 km and 56 km the SNR at the receiver with an
34
omnidirectioanl antenna is −15.1 dB, if the filter at the receiver has a bandwidth
of 150 kHz. The SNR at the detection stage, where the detection filter
bandwidth is taken to be 30 kHz, is calculated to be −9.9 dB.
35
Chapter 4
Range Resolution Improvement
4.1 Range Resolution Limitations When Single FM Channel Is Employed In the following simulations a 100ms monophonic sound file is low-pass filtered
at 15 kHz and frequency modulated using MATLAB’s fmmod operation, the
carrier frequency being 100 kHz and sampling frequency being 5 MHz. The
resulting wave has maximum frequency deviation of ±75 kHz. No pre-
emphasizing is employed.
The resulting signal is transmitted at 1 kW power with an omni-directional
antenna having a gain of 1.64. The signal is assumed to be reflected from two
targets, having radar cross sections of 10 m2, at distances of 50 km and 56 km
from the transmitter. Since the modulation bandwidth of the FM signal is about
25 kHz, the range resolution is expected to be 6 km. The two targets should be
successfully detected as being two separate targets. The received signals, which
are delayed in accordance of the target distances, have powers calculated by the
Radar Equation:
( ) 43
2
4 rGGP
P rttr π
σλ= (4.1)
Pr: received power
Pt: transmitted power
Gt: transmitter antenna gain
Gr: receiver antenna gain
λ: wavelength
36
σ: radar cross section of the target
r: target’s distance to the receiver
The transmitter and receiver antennas are assumed to be co-sited. Assuming the
radio station is transmitting at 100 MHz the wavelength is taken to be 3 m (since
the commercial FM radio broadcast spectrum is 88-108 MHz the wavelength
varies between 2.78 m and 3.41 m). The receiver antenna gain is taken to be 1,
although directional antennas may be employed. Since only one FM station is
used the bandwidth of the filter at the receiver is taken to be 150 kHz. Noise
power, which is obtained by kT0B, is added to the signal. The SNR is calculated
to be -15.1 dB.
The direct signal is taken to be the transmitter signal, because of the co-sited
transmitter receiver pair. The detection is done as explained in 3.4.
The autocorrelation of the directly received signal is depicted in Figure 4.1
0 1 2 3 4 5 6 7 8 9 10
x 105
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1Autocorrelation of the Direct Signal
time shift
ampl
itude 4.828 4.83 4.832 4.834 4.836 4.838 4.84
x 105
-1
-0.5
0
0.5
1
Figure 4.1 The autocorrelation of the directly received signal
37
The cross-correlation of the direct signal and target scattered signal is shown in
Figure 4.2.
0 2 4 6 8 10 12
x 105
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1Crosscorrelation of the Direct and Target Scattered Signals
time shift
ampl
itude
5.028 5.03 5.032 5.034 5.036 5.038 5.04 5.042 5.044
x 105
-1
-0.5
0
0.5
1
Figure 4.2 The cross-correlation of the direct signal and target scattered signal
At this stage, we take the Fourier transforms of the auto- and cross-correlation
functions. We divide the Fourier transform of the cross-correlation function to
the Fourier transform of the autocorrelation function. The resulting signal is
passed through a band-pass filter centered at the carrier frequency of the FM
station, 100 kHz in our case.
The one-sided spectra of the autocorrelation and cross-correlation functions, as
well as the resulting spectrum of the division operation, after the filtering
operation, is shown on Figure 4.3.
38
Figure 4.3 (a) One-sided spectrum of the autocorrelation of the direct signal. (b) One-sided
spectrum of the cross-correlation of the direct and target scattered signals. (c) One-sided
spectrum of the signal resulting from the division of auto- and cross-correlation spectrums after
filtering operation.
By taking the inverse FFT (IFFT) of the division signal and plotting the real part
of it Figure 4.4 is obtained.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Real part of the IFFT of the Division Signal
Figure 4.4. Real part of the IFFT of the division signal (1FM channel employed, targets are 6 km
separated, rectangular filter)
39
Figure 4.4 shows two peaks at delay bins 1667 and 1867. These two bins
correspond to the distances 50.01 km and 56.01 km. Hence, the two targets are
successfully detected at their true locations.
If the division signal is tapered (Figure 4.5), then the range resolution can be
seen clearer (Figure 4.6). This comes to a cost of increased peak width. Two
peaks are yet distinguishable.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 105
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency (Hz)
ampl
itude
Tapered Division Signal
Figure 4.5 Tapered division signal
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 6 km separated targets with 1 FM (Gaussian filter)
Figure 4.6 Real part of the IFFT of the division signal (1FM channel employed, targets are 6 km
separated, tapered division signal)
40
Simulations show that down to -30 dB SNR, which corresponds to a distance of
110 km from the receiver, the peaks due to the targets are 3 dB higher than the
peaks due to noise. Thus a successful detection of two targets at 110 km distance
to the receiver, having 6 km separation is possible with PCL employing single
channel FM.
The following simulations are done with the same specifications. This time
target1 and target2 are placed at 50 km and 53 km, respectively. Since the range
resolution is around 6 km, we expect an error in the detection.
Figure 4.7 depicts the real part IFFT of the division signal where targets are 3km
separated.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency (Hz)
amplitu
de
Detection of 3km separated taregets with 1FM channel
Figure 4.7 Real part of the IFFT of the division signal (1FM channel employed, targets are 3 km
separated, rectangular filter)
From Figure 4.7 it can be seen that the two targets are no more detected as being
two separate targets. Targets should be found in delay bins 1667 and 1700.
Although the largest peak is found at 1667, there are further peaks at 1717,
1767, and 1618, which are false alarms. Furthermore, the second target is not
detected at all. As expected, with one channel FM, targets that are closer to each
other than the range resolution cannot be successfully distinguished as two
separate targets.
41
If the division signal is tapered (Figure 4.8), it can be seen that the two peaks
have merged to one single peak, which makes the detection of individual targets
impossible.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 3 km separated targets with 1 FM (Gaussian filter)
Figure 4.8 Real part of the IFFT of the division signal (1FM channel employed, targets are 3 km
separated, tapered division signal)
4.2 Range Resolution Improvement When 3 FM Channels Are Employed In the following simulations three 100ms monophonic sound files with different
contents are each low-pass filtered at 15 kHz and frequency modulated using
MATLAB’s fmmod operation. The carrier frequencies are taken to be 100 kHz,
300 kHz and 400 kHz to simulate a weakly regulated environment. The
sampling frequency is 5 MHz. The resulting waves have maximum frequency
deviation of ±75 kHz. No pre-emphasizing is employed.
The resulting signal is transmitted at 1 kW power with an omni-directional
antenna having a gain of 1.64. The signal is assumed to be reflected from two
targets, having radar cross sections of 10 m2, at distances of 50 km and 56 km
from the transmitter. Since three FM station are used, the bandwidth of the filter
at the receiver is taken to be 500 kHz. Noise power, which is obtained by kT0B,
is added to the signal. The SNR is calculated to be -20.4 dB. The reduction in
42
the SNR is due to the fact that the noise power increased with the wider filter
bandwidth, whereas the signal power did not change.
At the detection step, after the division of the cross-correlation FFT to the
autocorrelation FFT, the filter employed and the resulting spectrum are shown in
Figure 4.9.
Figure 4.9 IFFT of the Division Signal after rectangular filtering
The IFFT of the division signal is depicted in Figure 4.10.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Detection of 6 km separated targets wtih 3FM channels
delay bin number
amplitu
de
Figure 4.10 Real part of the IFFT of the division signal (3FM channels employed, targets are 6
km separated, rectangular filter)
43
Two targets are successfully detected at delay bins 1667 and 1867, which
correspond to the distances 50.01 km and 56.01 km, respectively.
If the division signal is tapered (Figure 4.11), range resolution can be seen
better.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 6 km separated targets with 3 FM (rectangular & Gaussian filter)
Figure 4.11 Real part of the IFFT of the division signal (3FM channels employed, targets are 6
km separated, tapered division signal)
This suggests that a more suitable filter can be found.
The following simulations are done with the same specifications. This time
target1 and target2 are placed at 50 km and 53 km, respectively. Since the total
bandwidth is increased, we expect an improvement in the range resolution so
that the two targets are clearly distinguishable.
Figure 4.12 depicts the real part IFFT of the division signal, where targets are
3km separated, and 3FM channels are employed.
44
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 3 km spaced targets with 3 FM channels
Figure 4.12 Real part of the IFFT of the division signal (3FM channels employed, targets are 3
km separated, rectangular filter)
As seen from Figure 4.12 two targets at delay bins 1667 and 1767, which
correspond to distances 50.01 km and 53.01 km, respectively, are successfully
detected.
Simulations show that down to -33 dB SNR, which corresponds to a distance of
110 km from the receiver, the peaks due to the targets are 3 dB higher than the
peaks due to noise. Thus a successful detection of two targets at 110 km distance
to the receiver, having 3 km separation is possible with PCL employing 3 FM
channels.
If instead of rectangular filter, a Gaussian filter is employed (Figure 4.13) the
range resolution can be seen better.
45
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 3 km separated targets with 3 FM (Gaussian filter)
Figure 4.13 Real part of the IFFT of the division signal (3FM channels employed, targets are 3
km separated, Gaussian filter)
4.3 Range Resolution Improvement When 7 FM Channels Are Employed If it is possible to employ 7 FM channels, the range resolution can be made
better, as depicted in Figure 4.14. Two targets are placed at 50 km and 50.1 km.
Filter bandwidth is taken to be 1500 kHz. SNR is -24.4 dB.
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
delay bin number
amplitu
de
Detection of 100 m spaced targets with 7 FM channels
1650 1655 1660 1665 1670 1675 1680 1685 16900.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Figure 4.14 Real part of the IFFT of the division signal (7FM channels employed, targets are
100 m separated)
46
Two targets at delay bins 1667 and 1670, which correspond to distances 50.01
and 50.11 km, respectively, are successfully detected (Figure 4.14).
47
Chapter 5
Conclusions
Due to the increase in low cost digital signal processing power, Passive
Coherent Location radar systems become more and more attractive for military
as well as civilian applications. The passiveness of the system makes it an
excellent choice for surveillance. Currently extensive research is done on
improving PCL based systems.
FM radio broadcast signals are one of the appealing sets of non-cooperative
illuminators for PCL. It has a reasonably good Doppler resolution, but a poor
range resolution. Range resolution is inversely proportional to the transmitted
signal bandwidth. The poor range resolution is due to the fact that only a
fraction of the whole channel bandwidth, the modulation bandwidth, can be
effectively used.
In this thesis, a system that employs multiple FM radio channels, instead of a
single FM radio channel, is proposed and analyzed. A single FM radio channel
based PCL, that can have 25 kHz modulation bandwidth, shows 6 km range
resolution. It is shown that by employing 3 FM channels, two targets that are at
ranges 50 km and 53 km from the receiver are successfully detected. When 7
FM channels are employed, two targets that are at ranges 50 km and 50.1 km
from the receiver are successfully detected.
Simulations show that down to -33 dB SNR, which corresponds to a distance of
110 km from the receiver, the peaks due to the targets are 3 dB higher than the
peaks due to noise. Thus a successful detection of two targets at 110 km distance
to the receiver, having 3 km separation is possible.
48
In the detection, we employed rectangular and Gaussian tapered filters with
fixed bandwidth. However, a dynamic filtering is likely to achieve better results,
since the modulation bandwidth is a function of the content that is being
broadcasted. A better filtering process should involve a measurement of the
current modulation bandwidth on each channel and adjust the filter bandwidth
accordingly.
The detection algorithm is based on a simple linear operation. A more
sophisticated detection algorithm for time delay estimation can be utilized, such
as nonnegative deconvolution [31]. A more complex detection and resolution
refinement algorithm can be developed that first detects a target with a single
FM channel utilizing CFAR detection, then utilizes multiple radio channels to
refine the resolution of the area where the target was detected. This should
decrease the computational complexity of the system.
In this thesis, only monostatic geometry is analyzed. It is expected that range
resolution can be further improved when signals from multiple transmitters are
employed with a single or multiple receiver configuration.
49
APPENDIX A Here are two of the basic properties of Ambiguity function along with their
proofs. Two other properties, which are not used in this thesis, are mentioned, but
not proven here.
Property 1: Maximum at (0,0)
222 )0,0(),( sEAA =≤υτ (A.1)
Where sE is the energy of the signal. Proof: We apply the Schwarz inequality to Equation A.1:
222
22
22
)()(
)2exp()()(
)2exp()()(),(
sEdttsdtts
dttjtsdtts
dttjtstsA
=+=
+≤
+=
∫∫
∫ ∫
∫
∞
∞−
∗∞
∞−
∞
∞−
∞
∞−
∗
∞
∞−
∗
τ
πυτ
πυτυτ
(A.2)
The imequality in Equation A.2 is replaced with an equality if the functions in the two integrals are the conjugates of each other, i.e:
[ ] )2exp()()2exp()()( tjtstjtsts πυτπυτ −+=+=∗∗ (A.3)
This only happens when τ = 0 and υ = 0. Hence,
222 )0,0(),( sEAA =≤υτ
Property 2: Symmetry with respect to the origin
22 ),(),( υτυτ AA =−− (A.4) Proof: Set –τ and –υ in the equation for ambiguity function
50
22 )2exp()()(),( ∫
∞
∞−
∗ −−=−− dttjtstsA πυτυτ (A.5)
and make a change of variable, t’= t - τ
( )
( )
( ) 222
2
22
),(),(2exp)()(
''2exp)'()'()2exp(
')'(2exp)'()'(),(
υτυτπυτ
πυτπτ
τπυτυτ
AAdttjtsts
dttjtstsj
dttjtstsA
==−+=
−+−=
+−+=−−
∗∞
∞−
∗
∞
∞−
∗
∞
∞−
∗
∫
∫
∫
(A.6)
Hence,
22 ),(),( υτυτ AA =−−
Property 3: Constant volume
22 .),( sEddA =∫ ∫∞
∞−
∞
∞−
υτυτ (A.7)
Where sE is the energy of the signal.
Property 4: Linear FM effect If 2),( υτA is the ambiguity function for the signal )(ts
2),()( υτAts ⇔ then adding linear frequency modulation (LFM) implies that
22 ),()exp()( βτυτπβ −⇔ Atjts (A.8)
51
APPENDIX B
Frequency Modulation Frequency modulation (FM) is a non-linear modulation technique that is been
used since early 1930s in radio transmission. Together with phase modulation
(PM), FM is referred as angle modulation, because the angle of the carrier signal
is changed according to the message signal. In FM, this change is done by
changing the frequency (fc) of the carrier signal according to the variations in the
message signal. As will be explained in detail later, due to the bandwidth-
expansion property of angle modulation, the bandwidth of the modulated signal
can be many times higher than the bandwidth of the message signal. As a result
of this property, the noise immunity of the signal is increased substantially, what
makes angle modulation an adequate tool for high-fidelity (HI-FI) music
broadcasting and point-to-point low power communication.
The general representation of an angle-modulated signal is
))(2cos())(cos()( ttfAtAtu ccc φπθ +== (B.1)
where cA is the carrier amplitude, cf is the carrier frequency, )(tθ is the angle as
a function of time, and )(tφ is the phase as a function of time. The instantaneous
frequency of the signal is given by
)(21)(
21)( t
dtdft
dtdtf ci φ
πθ
π+== (B.2)
If a message signal m(t) is frequency modulated then
∫∞−
=t
f dmkt ττπφ )(2)( (B.3)
where fk is the frequency deviation constant. Thus the instantaneous frequency
becomes
52
∫∞−
+=t
fci dmdtdkftf ττ )()( (B.4)
From equation (2.2.4) it can be seen that the frequency of the modulated signal
is changed according to the variations in the message signal. The maximum
frequency deviation is given by
[ ])(maxmax tmkf f=∆ (B.5)
The maximum frequency deviation divided by the bandwidth of the message
signal defines a number called the modulation index.
[ ]W
tmkWf f
f
)(maxmax =∆
=β (B.6)
The modulation index is helpful in estimating the bandwidth of the FM signal as
will be seen later.
Due to the non-linearity of FM, the spectral characteristic of an FM signal is
mathematically problematic to express. The method followed is generally the
study of FM signals with simple message signals and making certain
approximations.
First, we begin with the case where the message signal is a sinusoid
)2cos()( tfatm mπ= (B.7)
From equation (2.2.3) we see that
)2sin()2cos(2)( tff
akdfakt m
m
ft
mf πττππφ == ∫∞−
(B.8)
Thus, according to equation (2.2.1) the frequency modulated signal can be
expressed as
( )))2sin(exp()2exp(Re
))2sin(2cos()(
tfjtfjA
tftfAtu
mcc
mfcc
πβπ
πβπ
=
+= (B.9)
))2sin( tfmπ in equation (2.2.9) is periodic and can therefore be expanded in a
Fourier series representation, where the Fourier series coefficients are
53
∫
∫
−=
−=
πβ
π
ππβ
2
0
0
))sin(exp(21
)2exp())2sin(exp(
1
dunuuj
dttfjntfjfc mmmn
mf
(B.10)
where tfu mπ2= . The integral in equation (2.2.10) is known as the Bessel
function of the first kind of order n and is denoted by )(βnJ . Thus the
modulated signal can be expressed as
∑∞
−∞=
+=n
mcnc tnffJAtu ))(2cos()()( πβ (B.11)
Since the running index in equation (2.2.11) runs from minus infinity to infinity,
the signal contains harmonics at mc nff + , which means that the bandwidth of the
signal is infinity. However, )(βnJ decreases for increasing n. Thus, the
amplitudes for harmonics far away from fc are very small. For small β, )(βnJ
can be approximated as
!2)(
nJ n
n
nββ ≈ (B.12)
In general, the bandwidth, in which at least 98% of the total signal power lies is
accepted as the effective bandwidth (BW), given by
)(2)1(2 mfmf fakfBW +=+= β (B.13)
For a general non-periodic message signal the modulated signal has no simple
expression. However, there is an approximate relation for the effective
bandwidth of the modulated signal if the message signal is band-limited. This
relation is known as the Carson’s rule, and is given by
[ ]W
Wtmk
WBW ff ⎟
⎟⎠
⎞⎜⎜⎝
⎛+=+= 1
)(max2)1(2 β (B.14)
where W is the message signal bandwidth. As seen from equation (2.2.14), the
increase of message signal bandwidth results in an additive increase in effective
modulated signal bandwidth.
54
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